Internet connected auxiliary power unit airline maintenance system

1. A computer-implemented method for generating maintenance recommendation for an auxiliary power unit (APU), the method comprising: transmitting, by one or more processors, digitized APU data to an edge node; transmitting, by the one or more processors, one or more tuning parameters from a computer-implemented cloud-based health monitoring system to the edge node, the one or more tuning parameters including a threshold level that is adjustable by the computer-implemented cloud-based health monitoring system based on the digitized APU data; applying, by the one or more processors at the edge node, an algorithm that analyzes the digitized APU data using the one or more tuning parameters to capture only elements of the digitized APU data that indicate an APU health concern, wherein the algorithm is configured to remove digitized APU data indicating intermittent or false-positive APU health concerns, resulting in reduced time to capture and evaluate APU data; storing, by the one or more processors, the digitized APU data that indicate the APU health concern in a buffer; applying, by the one or more processors, a clustering technique including a support vector machine to the APU data in the buffer to distinguish a true APU health concern from intermittent or false-positive APU health concerns, wherein a stop timer is used to end the application of the clustering technique after a pre-determined time period has elapsed; and transmitting, by the one or more processors, the captured elements indicating the true APU health concern to the computer-implemented cloud-based health monitoring system.

2. The computer-implemented method of claim 1 , further comprising: providing, by the one or more processors, a plurality of descriptions of an APU fault; receiving, by the one or more processors, a selection of a common fault phenomenon identification from the plurality of descriptions of an APU fault; generating, by the one or more processors, an APU fault report from the digitized APU data and the common fault phenomenon identification from the plurality of descriptions of an APU fault; and transmitting, by the one or more processors, the APU fault report to the computer-implemented cloud-based health monitoring system to generate a maintenance recommendation.

3. The computer-implemented method of claim 2 , wherein the computer-implemented cloud-based health monitoring system is configured to analyze the digitized APU data and information from the APU fault report to update the one or more tuning parameters and to generate a maintenance recommendation.

4. The computer-implemented method of claim 3 , further include: generating, by the one or more processors, a digitized task card that identifies maintenance actions to be performed based on the maintenance recommendation from the computer-implemented cloud-based health monitoring system.

5. The computer-implemented method of claim 1 , further comprising: applying, by the one or more processors to the algorithm, a sliding window algorithm to identify a subset of the digitized APU data to analyze; applying, by the one or more processors, a likelihood function to the digitized APU data in the sliding window to identify data indicating potential APU health concerns; and storing, by the one or more processors, the data indicating potential APU health concerns in the buffer.

6. The computer-implemented method of claim 1 , wherein the edge node is one of a discrete unit on an aircraft or integrated with airplane condition monitoring system (ACMS).

7. A computer-implemented system for generating maintenance recommendation for an auxiliary power unit (APU), the computer-implemented system comprising: a memory having processor-readable instructions stored therein; and at least one processor configured to access the memory and execute the processor-readable instructions, which when executed by the processor configures the processor to perform functions for: transmitting digitized APU data to an edge node; transmitting one or more tuning parameters from a computer-implemented cloud-based health monitoring system to the edge node, the one or more tuning parameters including a threshold level that is adjustable by the computer-implemented cloud-based health monitoring system based on the digitized APU data; applying, at the edge node, an algorithm that analyzes the digitized APU data using the one or more tuning parameters to capture only elements of the digitized APU data that indicate an APU health concern, wherein the algorithm is configured to remove digitized APU data indicating intermittent or false-positive APU health concerns, resulting in reduced time to capture and evaluate APU data; storing the digitized APU data that indicate the APU health concern in a buffer; applying a clustering technique including a support vector machine to the APU data in the buffer to distinguish a true APU health concern from intermittent or false-positive APU health concerns, wherein a stop timer is used to end the application of the clustering technique after a pre-determined time period has elapsed; and transmitting the captured elements indicating the true APU health concern to the computer-implemented cloud-based health monitoring system.

8. The computer-implemented system of claim 7 , further comprising: providing a plurality of descriptions of an APU fault; receiving a selection of a common fault phenomenon identification from the plurality of descriptions of an APU fault; generating an APU fault report from the digitized APU data and the common fault phenomenon identification from the plurality of descriptions of an APU fault; and transmitting the APU fault report to the computer-implemented cloud-based health monitoring system to generate a maintenance recommendation.

9. The computer-implemented system of claim 8 , wherein the computer-implemented cloud-based health monitoring system is configured to analyze the digitized APU data and information from the APU fault report to update the one or more tuning parameters and to generate a maintenance recommendation.

10. The computer-implemented system of claim 9 , further include: generating a digitized task card that identifies maintenance actions to be performed based on the maintenance recommendation from the computer-implemented cloud-based health monitoring system.

11. The computer-implemented system of claim 7 , further comprising: applying a sliding window algorithm to identify a subset of the digitized APU data to analyze; applying a likelihood function to the digitized APU data in the sliding window to identify data indicating potential APU health concerns; and storing the data indicating potential APU health concerns in the buffer.

12. The computer-implemented system of claim 7 , wherein the edge node is one of a discrete unit on an aircraft or integrated with airplane condition monitoring system (ACMS).

13. A non-transitory computer-readable medium containing instructions for generating maintenance recommendation for an auxiliary power unit (APU), the non-transitory computer-readable medium storing instruction that, when executed by at least one processor, configure the at least one processor to perform: transmitting, by one or more processors, digitized APU data to an edge node; transmitting, by the one or more processors, one or more tuning parameters from a computer-implemented cloud-based health monitoring system to the edge node, the one or more tuning parameters including a threshold level that is adjustable by the computer-implemented cloud-based health monitoring system based on the digitized APU data; applying, by the one or more processors at the edge node, an algorithm that analyzes the digitized APU data using the one or more tuning parameters to capture only elements of the digitized APU data that indicate an APU health concern, wherein the algorithm is configured to remove digitized APU data indicating intermittent or false-positive APU health concerns, resulting in reduced time to capture and evaluate APU data; storing, by the one or more processors, the digitized APU data that indicate the APU health concern in a buffer; applying, by the one or more processors, a clustering technique including a support vector machine to the APU data in the buffer to distinguish a true APU health concern from intermittent or false-positive APU health concerns, wherein a stop timer is used to end the application of the clustering technique after a pre-determined time period has elapsed; and transmitting, by the one or more processors, the captured elements indicating the true APU health concern to the computer-implemented cloud-based health monitoring system.

14. The non-transitory computer-readable medium of claim 13 , further comprising: providing, by the one or more processors, a plurality of descriptions of an APU fault; receiving, by the one or more processors, a selection of a common fault phenomenon identification from the plurality of descriptions of an APU fault; generating, by the one or more processors, an APU fault report from the digitized APU data and the common fault phenomenon identification from the plurality of descriptions of an APU fault; and transmitting, by the one or more processors, the APU fault report to the computer-implemented cloud-based health monitoring system to generate a maintenance recommendation.

15. The non-transitory computer-readable medium of claim 14 , wherein the computer-implemented cloud-based health monitoring system is configured to analyze the digitized APU data and information from the APU fault report to update the one or more tuning parameters and to generate a maintenance recommendation.

16. The non-transitory computer-readable medium of claim 15 , further include: generating, by the one or more processors, a digitized task card that identifies maintenance actions to be performed based on the maintenance recommendation from the computer-implemented cloud-based health monitoring system.

17. The non-transitory computer-readable medium of claim 13 , further comprising: applying, by the one or more processors to the algorithm, a sliding window algorithm to identify a subset of the digitized APU data to analyze; applying, by the one or more processors, a likelihood function to the digitized APU data in the sliding window to identify data indicating potential APU health concerns; and storing, by the one or more processors, the data indicating potential APU health concerns in the buffer.